The invention relates generally to analysis of defects in manufactured objects, and more particularly to a method of detecting defect patterns in semiconductor devices which have undergone manufacturing processes in which the semiconductor device has been oriented in a random manner relative to processing equipment.
Semiconductor manufacturing is a highly complex process, comprising on the order of several hundred individual manufacturing steps. In any given manufacturing step, numerous factors can lead to defects in the semiconductor wafers; for example, an improperly adjusted tool or contamination of the wafer. Given the complexity of the manufacturing process, with literally thousands of factors potentially resulting in a defect, it is known to employ statistical methods generally, and data mining in particular, in the analysis of defects in the semiconductor wafer manufacturing process.
Data mining is a process of searching large volumes of data for patterns. In the context of semiconductor wafer manufacturing processing operations (such as Chemical Mechanical Polishing (CMP) operations) in which defects resulting from the operation are detectable on the wafer surface, it is known to obtain a plurality of images, each of a surface (or portion thereof) of a wafer, and to combine the plurality of images into a single composite image. Non-random distributions of defects in the composite image may suggest, for example, a specific problem with a specific process, based on factors such as the type or the position of the distribution of defects.
A composite image formed as part of the known data mining process is meaningful if the defects are related by some common factor, such as the geometry of the manufactured object. That is, data from one manufactured object to another is comparable if the data is uniformly referenced to (or oriented with respect to) the invariant geometry of the manufactured objects. Stated otherwise, the composite data is meaningful if each of the plurality of images reflects each semiconductor wafer having been positioned relative to a given tool is a single, known orientation. While in some of the semiconductor manufacturing processes each wafer is oriented relative to the tool in the same way, this is not true of all manufacturing processes. For example, in a rotational polishing operation, the initial angular orientation of the semiconductor wafer relative to the polishing tool may be random. Thus, data pertaining to manufacturing operations in which orientation of the manufactured object relative to the manufacturing tool is random is less valuable for data mining.
A need exists, therefore, for a method and system to allow data from manufacturing processes in which a manufactured object is capable of being randomly positioned relative to a manufacturing tool to be oriented to a common reference for data mining analysis. Additionally, given that the defects of interest may cover a broad range of sizes from the micro to the macro, a method and system applicable to both localized defects (local scratches, buildup of foreign material, pullouts, or microbumps) as well as defects covering a larger portion of a surface of a semiconductor wafer (tool related defects such as film thicknesses over platen hotspots or CMP wafer holder non-uniformities) would be especially desirable.